In [1]:
## 設定
verbose = False
### 言語の割合の均等化
balanced = True
### LDA 用
## トピック数
n_topics = 15 # 30は多過ぎる?
## doc, term の設定
doc_type = 'form'
doc_attr = 'sound'
max_doc_size = 12
##
term_size = 'character'
term_type = '1gram'
## skippy n-gram の結合範囲
max_distance_val = round(max_doc_size * 0.8)
print(f"max_distance_val: {max_distance_val}")
## ngram を包括的にするかどうか
ngram_is_inclusive = True
### DTM 構築
## term の最低頻度
term_min_freq = 2
## 高頻度 term の濫用指標: 大きくし過ぎないように.0.05 は十分に大きい
term_abuse_threshold = 0.05
max_distance_val: 10
In [2]:
import sys, os, random, re, glob
import pandas as pd
import pprint as pp
from functools import reduce
In [3]:
## load data to process
from pathlib import Path
import pprint as pp
wd = Path(".")
##
dirs = [ x for x in wd.iterdir() if x.is_dir() and not x.match(r"plot*") ]
if verbose:
print(f"The following {len(dirs)} directories are potential targets:")
pp.pprint(dirs)
## list up files in target directory
wd = Path(".")
target_dir = "data-words" # can be changed
target_files = sorted(list(wd.glob(f"{target_dir}/*.csv")))
#
print(f"\n{target_dir} contains {len(target_files)} files to process")
pp.pprint(target_files)
data-words contains 10 files to process
[PosixPath('data-words/base-sound-English-r6e-originals.csv'),
PosixPath('data-words/base-sound-French-r0-sample900.csv'),
PosixPath('data-words/base-sound-German-r1a-original.csv'),
PosixPath('data-words/base-spell-English-r6e-originals.csv'),
PosixPath('data-words/base-spell-Esperanto-r0-orginal.csv'),
PosixPath('data-words/base-spell-French-r0-originals.csv'),
PosixPath('data-words/base-spell-German-r1a-originals.csv'),
PosixPath('data-words/base-spell-Icelandic-r0-original.csv'),
PosixPath('data-words/base-spell-Russian-r0-originals.csv'),
PosixPath('data-words/base-spell-Swahili-r0-orginal.csv')]
In [4]:
import pandas as pd
## データ型の辞書
types = "spell sound freq".split(" ")
type_setting = { t : 0 for t in types }
print(type_setting)
## 言語名の辞書
langs = "english esperanto french german icelandic russian swahili".split(" ")
#langs = "english esperanto french german russian swahili".split(" ")
#langs = "english esperanto french german icelandic swahili".split(" ")
lang_setting = { lang : 0 for lang in langs }
print(lang_setting)
## 辞書と統合
settings = { 'form': None, **type_setting, **lang_setting }
print(settings)
{'spell': 0, 'sound': 0, 'freq': 0}
{'english': 0, 'esperanto': 0, 'french': 0, 'german': 0, 'icelandic': 0, 'russian': 0, 'swahili': 0}
{'form': None, 'spell': 0, 'sound': 0, 'freq': 0, 'english': 0, 'esperanto': 0, 'french': 0, 'german': 0, 'icelandic': 0, 'russian': 0, 'swahili': 0}
In [5]:
vars = list(settings.keys())
print(f"targe var names: {vars}")
d_parts = [ ]
for lang in langs:
local_settings = settings.copy()
print(f"processing: {lang}")
try:
for f in [ f for f in target_files if lang.capitalize() in str(f) ]:
print(f"reading: {f}")
# 言語名の指定
local_settings[lang] = 1
# 型名の指定
for type in vars:
if type in str(f):
local_settings[type] = 1
#
d = pd.read_csv(f, encoding='utf-8', sep = ",", on_bad_lines = 'skip') # Crucially, ...= skip
df = pd.DataFrame(d, columns = vars)
for var in [ var for var in (types + langs) if var != 'freq' ]:
df[var] = local_settings[var]
d_parts.append(df)
except IndexError:
pass
#
if verbose:
d_parts
targe var names: ['form', 'spell', 'sound', 'freq', 'english', 'esperanto', 'french', 'german', 'icelandic', 'russian', 'swahili'] processing: english reading: data-words/base-sound-English-r6e-originals.csv reading: data-words/base-spell-English-r6e-originals.csv processing: esperanto reading: data-words/base-spell-Esperanto-r0-orginal.csv processing: french reading: data-words/base-sound-French-r0-sample900.csv reading: data-words/base-spell-French-r0-originals.csv processing: german reading: data-words/base-sound-German-r1a-original.csv reading: data-words/base-spell-German-r1a-originals.csv processing: icelandic reading: data-words/base-spell-Icelandic-r0-original.csv processing: russian reading: data-words/base-spell-Russian-r0-originals.csv processing: swahili reading: data-words/base-spell-Swahili-r0-orginal.csv
In [6]:
## データ統合
raw_df = pd.concat(d_parts)
raw_df
Out[6]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
15223 rows × 11 columns
In [7]:
## 文字数の列を追加
raw_df['size'] = [ len(x) for x in raw_df[doc_type] ]
raw_df
Out[7]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
15223 rows × 12 columns
In [8]:
## 言語名= language の列を追加
check = False
language_vals = [ ]
for i, row in raw_df.iterrows():
if check:
print(row)
for j, lang in enumerate(langs):
if check:
print(f"{i}: {lang}")
if row[lang] == 1:
language_vals.append(lang)
if verbose:
print(language_vals)
len(language_vals)
#
raw_df['language'] = language_vals
raw_df
Out[8]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
15223 rows × 13 columns
In [9]:
## 言語の選別
select_languages = True
selected_langs = re.split(r",\s*", "english, french, german, russian, swahili")
print(f"selected languages: {selected_langs}")
if select_languages:
df_new = [ ]
for lang in selected_langs:
df_new.append(raw_df[raw_df[lang] == 1])
raw_df = pd.concat(df_new)
#
raw_df
selected languages: ['english', 'french', 'german', 'russian', 'swahili']
Out[9]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
13673 rows × 13 columns
In [10]:
## 文字数の分布
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.hist(raw_df['size'], bins = 40)
ax.set_xlabel('length of doc')
ax.set_ylabel('freq')
plt.title(f"Length distribution for docs")
fig.show()
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_13188/1088473461.py:12: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown fig.show()
In [11]:
## 長さで濾過
print(f"max doc size: {max_doc_size}")
original_size = len(raw_df)
raw_df = raw_df[raw_df['size'] < max_doc_size]
filtered_size = len(raw_df)
print(f"{original_size - filtered_size} cases removed")
max doc size: 12 365 cases removed
In [12]:
## 結果の検査 1
for lang in langs:
print(raw_df[lang].value_counts())
english 1 8249 0 5059 Name: count, dtype: int64 esperanto 0 13308 Name: count, dtype: int64 french 0 11492 1 1816 Name: count, dtype: int64 german 0 11743 1 1565 Name: count, dtype: int64 icelandic 0 13308 Name: count, dtype: int64 russian 0 12335 1 973 Name: count, dtype: int64 swahili 0 12603 1 705 Name: count, dtype: int64
In [13]:
## 結果の検査 2
for type in types:
print(raw_df[type].value_counts())
spell 1 7588 0 5720 Name: count, dtype: int64 sound 1 11630 0 1678 Name: count, dtype: int64 freq 1 12326 1 966 1 не 1 1 то время как 1 1 северу 1 1 него 1 1 будет 1 1 образом 1 1 мышь 1 Name: count, dtype: int64
In [14]:
## 統合: 割合補正を適用
eng_reduct_factor = 0.2
if balanced:
eng_df = raw_df[raw_df['english'] == 1]
non_eng_df = raw_df[raw_df['english'] == 0]
eng_reduced_df = eng_df.sample(round(len(eng_df) * eng_reduct_factor))
raw_df = pd.concat([eng_reduced_df, non_eng_df])
raw_df
Out[14]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2766 | twɛntɪ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| 2916 | tɹɑpɪkəl | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | english |
| 504 | dɪɹɛktəɹ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | english |
| 3094 | wɪŋ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | english |
| 4007 | ɹɛdɪ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
6709 rows × 13 columns
In [15]:
## 結果の検査 3
for lang in langs:
print(raw_df[lang].value_counts())
english 0 5059 1 1650 Name: count, dtype: int64 esperanto 0 6709 Name: count, dtype: int64 french 0 4893 1 1816 Name: count, dtype: int64 german 0 5144 1 1565 Name: count, dtype: int64 icelandic 0 6709 Name: count, dtype: int64 russian 0 5736 1 973 Name: count, dtype: int64 swahili 0 6004 1 705 Name: count, dtype: int64
In [16]:
## 順序のランダマイズ
import sklearn.utils
raw_df = sklearn.utils.shuffle(raw_df)
In [17]:
## データ名の指定
df = raw_df[raw_df[doc_attr] == 1]
print(f"doc_attr: {doc_attr}")
df
doc_attr: sound
Out[17]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3998 | unity | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english |
| 3972 | types | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english |
| 677 | manʃaft | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | german |
| 342 | maʁufləʁje | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 10 | french |
| 555 | fenêtre | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | french |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 674 | fʁakasɛ | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | french |
| 107 | fabriqué | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 8 | french |
| 734 | ʃpʁaːxe | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | german |
| 576 | siza | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | french |
| 1887 | nuz | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | english |
5031 rows × 13 columns
In [18]:
## ngram の追加
import sys
sys.path.append('..')
import re
import ngrams
import importlib
importlib.reload(ngrams)
import ngrams_skippy
bases = df[doc_type]
## 1gram 列の追加
#sep = r""
#unigrams = [ list(filter(lambda x: len(x) > 0, y)) for y in [ re.split(sep, z) for z in bases ] ]
unigrams = ngrams.gen_unigrams(bases, sep = r"", check = False)
if verbose:
random.sample(unigrams, 5)
#
df['1gram'] = unigrams
#df.loc[:,'1gram'] = unigrams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_13188/1248262955.py:21: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['1gram'] = unigrams
Out[18]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3998 | unity | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [u, n, i, t, y] |
| 3972 | types | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [t, y, p, e, s] |
| 677 | manʃaft | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | german | [m, a, n, ʃ, a, f, t] |
| 342 | maʁufləʁje | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 10 | french | [m, a, ʁ, u, f, l, ə, ʁ, j, e] |
| 555 | fenêtre | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | french | [f, e, n, ê, t, r, e] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 674 | fʁakasɛ | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | french | [f, ʁ, a, k, a, s, ɛ] |
| 107 | fabriqué | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 8 | french | [f, a, b, r, i, q, u, é] |
| 734 | ʃpʁaːxe | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | german | [ʃ, p, ʁ, a, ː, x, e] |
| 576 | siza | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | french | [s, i, z, a] |
| 1887 | nuz | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | english | [n, u, z] |
5031 rows × 14 columns
In [19]:
## 2gram列の追加
bigrams = ngrams.gen_bigrams(bases, sep = r"", check = False)
## 包括的 2gram の作成
if ngram_is_inclusive:
bigrams = [ [*b, *u] for b, u in zip(bigrams, unigrams) ]
if verbose:
print(random.sample(bigrams, 3))
In [20]:
df['2gram'] = bigrams
if verbose:
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_13188/1480305306.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['2gram'] = bigrams
In [21]:
## 3gram列の追加
trigrams = ngrams.gen_trigrams(bases, sep = r"", check = False)
## 包括的 3gram の作成
if ngram_is_inclusive:
trigrams = [ [ *t, *b ] for t, b in zip(trigrams, bigrams) ]
if verbose:
print(random.sample(trigrams, 3))
In [22]:
df['3gram'] = trigrams
if verbose:
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_13188/3715201492.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['3gram'] = trigrams
In [23]:
## skippy 2grams の生成
import sys
sys.path.append("..") # library path に一つ上の階層を追加
import ngrams_skippy
skippy_2grams = [ ngrams_skippy.generate_skippy_bigrams(x,
missing_mark = '…',
max_distance = max_distance_val, check = False)
for x in df['1gram'] ]
## 包括的 skippy 2-grams の生成
if ngram_is_inclusive:
for i, b2 in enumerate(skippy_2grams):
b2.extend(unigrams[i])
#
if verbose:
random.sample(skippy_2grams, 3)
In [24]:
## skippy 2gram 列の追加
df['skippy2gram'] = skippy_2grams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_13188/3263801935.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['skippy2gram'] = skippy_2grams
Out[24]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | 2gram | 3gram | skippy2gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3998 | unity | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [u, n, i, t, y] | [un, ni, it, ty, u, n, i, t, y] | [uni, nit, ity, un, ni, it, ty, u, n, i, t, y] | [un, u…i, u…t, u…y, ni, n…t, n…y, it, i…y, ty,... |
| 3972 | types | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [t, y, p, e, s] | [ty, yp, pe, es, t, y, p, e, s] | [typ, ype, pes, ty, yp, pe, es, t, y, p, e, s] | [ty, t…p, t…e, t…s, yp, y…e, y…s, pe, p…s, es,... |
| 677 | manʃaft | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | german | [m, a, n, ʃ, a, f, t] | [ma, an, nʃ, ʃa, af, ft, m, a, n, ʃ, a, f, t] | [man, anʃ, nʃa, ʃaf, aft, ma, an, nʃ, ʃa, af, ... | [ma, m…n, m…ʃ, m…a, m…f, m…t, an, a…ʃ, a…a, a…... |
| 342 | maʁufləʁje | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 10 | french | [m, a, ʁ, u, f, l, ə, ʁ, j, e] | [ma, aʁ, ʁu, uf, fl, lə, əʁ, ʁj, je, m, a, ʁ, ... | [maʁ, aʁu, ʁuf, ufl, flə, ləʁ, əʁj, ʁje, ma, a... | [ma, m…ʁ, m…u, m…f, m…l, m…ə, m…j, m…e, aʁ, a…... |
| 555 | fenêtre | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | french | [f, e, n, ê, t, r, e] | [fe, en, nê, êt, tr, re, f, e, n, ê, t, r, e] | [fen, enê, nêt, êtr, tre, fe, en, nê, êt, tr, ... | [fe, f…n, f…ê, f…t, f…r, f…e, en, e…ê, e…t, e…... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 674 | fʁakasɛ | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | french | [f, ʁ, a, k, a, s, ɛ] | [fʁ, ʁa, ak, ka, as, sɛ, f, ʁ, a, k, a, s, ɛ] | [fʁa, ʁak, aka, kas, asɛ, fʁ, ʁa, ak, ka, as, ... | [fʁ, f…a, f…k, f…s, f…ɛ, ʁa, ʁ…k, ʁ…a, ʁ…s, ʁ…... |
| 107 | fabriqué | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 8 | french | [f, a, b, r, i, q, u, é] | [fa, ab, br, ri, iq, qu, ué, f, a, b, r, i, q,... | [fab, abr, bri, riq, iqu, qué, fa, ab, br, ri,... | [fa, f…b, f…r, f…i, f…q, f…u, f…é, ab, a…r, a…... |
| 734 | ʃpʁaːxe | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | german | [ʃ, p, ʁ, a, ː, x, e] | [ʃp, pʁ, ʁa, aː, ːx, xe, ʃ, p, ʁ, a, ː, x, e] | [ʃpʁ, pʁa, ʁaː, aːx, ːxe, ʃp, pʁ, ʁa, aː, ːx, ... | [ʃp, ʃ…ʁ, ʃ…a, ʃ…ː, ʃ…x, ʃ…e, pʁ, p…a, p…ː, p…... |
| 576 | siza | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | french | [s, i, z, a] | [si, iz, za, s, i, z, a] | [siz, iza, si, iz, za, s, i, z, a] | [si, s…z, s…a, iz, i…a, za, s, i, z, a] |
| 1887 | nuz | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | english | [n, u, z] | [nu, uz, n, u, z] | [nuz, nu, uz, n, u, z] | [nu, n…z, uz, n, u, z] |
5031 rows × 17 columns
In [25]:
## skippy 3grams の生成
import sys
sys.path.append("..") # library path に一つ上の階層を追加
import ngrams_skippy
skippy_3grams = [ ngrams_skippy.generate_skippy_trigrams(x,
missing_mark = '…',
max_distance = max_distance_val, check = False)
for x in df['1gram'] ]
## 包括的 skippy 3-grams の生成
if ngram_is_inclusive:
for i, t2 in enumerate(skippy_3grams):
t2.extend(skippy_2grams[i])
#
if verbose:
random.sample(skippy_3grams, 3)
In [26]:
## skippy 3gram 列の追加
df['skippy3gram'] = skippy_3grams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_13188/1159231133.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['skippy3gram'] = skippy_3grams
Out[26]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | 2gram | 3gram | skippy2gram | skippy3gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3998 | unity | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [u, n, i, t, y] | [un, ni, it, ty, u, n, i, t, y] | [uni, nit, ity, un, ni, it, ty, u, n, i, t, y] | [un, u…i, u…t, u…y, ni, n…t, n…y, it, i…y, ty,... | [uni, un…t, un…y, u…it, u…i…y, u…ty, nit, ni…y... |
| 3972 | types | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [t, y, p, e, s] | [ty, yp, pe, es, t, y, p, e, s] | [typ, ype, pes, ty, yp, pe, es, t, y, p, e, s] | [ty, t…p, t…e, t…s, yp, y…e, y…s, pe, p…s, es,... | [typ, ty…e, ty…s, t…pe, t…p…s, t…es, ype, yp…s... |
| 677 | manʃaft | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | german | [m, a, n, ʃ, a, f, t] | [ma, an, nʃ, ʃa, af, ft, m, a, n, ʃ, a, f, t] | [man, anʃ, nʃa, ʃaf, aft, ma, an, nʃ, ʃa, af, ... | [ma, m…n, m…ʃ, m…a, m…f, m…t, an, a…ʃ, a…a, a…... | [man, ma…ʃ, ma…a, ma…f, ma…t, m…nʃ, m…n…a, m…n... |
| 342 | maʁufləʁje | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 10 | french | [m, a, ʁ, u, f, l, ə, ʁ, j, e] | [ma, aʁ, ʁu, uf, fl, lə, əʁ, ʁj, je, m, a, ʁ, ... | [maʁ, aʁu, ʁuf, ufl, flə, ləʁ, əʁj, ʁje, ma, a... | [ma, m…ʁ, m…u, m…f, m…l, m…ə, m…j, m…e, aʁ, a…... | [maʁ, ma…u, ma…f, ma…l, ma…ə, ma…ʁ, ma…j, ma…e... |
| 555 | fenêtre | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | french | [f, e, n, ê, t, r, e] | [fe, en, nê, êt, tr, re, f, e, n, ê, t, r, e] | [fen, enê, nêt, êtr, tre, fe, en, nê, êt, tr, ... | [fe, f…n, f…ê, f…t, f…r, f…e, en, e…ê, e…t, e…... | [fen, fe…ê, fe…t, fe…r, fe…e, f…nê, f…n…t, f…n... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 674 | fʁakasɛ | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | french | [f, ʁ, a, k, a, s, ɛ] | [fʁ, ʁa, ak, ka, as, sɛ, f, ʁ, a, k, a, s, ɛ] | [fʁa, ʁak, aka, kas, asɛ, fʁ, ʁa, ak, ka, as, ... | [fʁ, f…a, f…k, f…s, f…ɛ, ʁa, ʁ…k, ʁ…a, ʁ…s, ʁ…... | [fʁa, fʁ…k, fʁ…a, fʁ…s, fʁ…ɛ, f…ak, f…a…a, f…a... |
| 107 | fabriqué | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 8 | french | [f, a, b, r, i, q, u, é] | [fa, ab, br, ri, iq, qu, ué, f, a, b, r, i, q,... | [fab, abr, bri, riq, iqu, qué, fa, ab, br, ri,... | [fa, f…b, f…r, f…i, f…q, f…u, f…é, ab, a…r, a…... | [fab, fa…r, fa…i, fa…q, fa…u, fa…é, f…br, f…b…... |
| 734 | ʃpʁaːxe | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | german | [ʃ, p, ʁ, a, ː, x, e] | [ʃp, pʁ, ʁa, aː, ːx, xe, ʃ, p, ʁ, a, ː, x, e] | [ʃpʁ, pʁa, ʁaː, aːx, ːxe, ʃp, pʁ, ʁa, aː, ːx, ... | [ʃp, ʃ…ʁ, ʃ…a, ʃ…ː, ʃ…x, ʃ…e, pʁ, p…a, p…ː, p…... | [ʃpʁ, ʃp…a, ʃp…ː, ʃp…x, ʃp…e, ʃ…ʁa, ʃ…ʁ…ː, ʃ…ʁ... |
| 576 | siza | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | french | [s, i, z, a] | [si, iz, za, s, i, z, a] | [siz, iza, si, iz, za, s, i, z, a] | [si, s…z, s…a, iz, i…a, za, s, i, z, a] | [siz, si…a, s…za, iza, si, s…z, s…a, iz, i…a, ... |
| 1887 | nuz | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | english | [n, u, z] | [nu, uz, n, u, z] | [nuz, nu, uz, n, u, z] | [nu, n…z, uz, n, u, z] | [nuz, nu, n…z, uz, n, u, z] |
5031 rows × 18 columns
In [27]:
## LDA 構築の基になる document-term matrix (dtm) を構築
from gensim.corpora.dictionary import Dictionary
bots = df[term_type]
diction = Dictionary(bots)
## 結果の確認
print(diction)
Dictionary<80 unique tokens: ['i', 'n', 't', 'u', 'y']...>
In [28]:
## diction の濾過
import copy
diction_copy = copy.deepcopy(diction)
## filter適用: 実は諸刃の刃で,token数が少ない時には適用しない方が良い
print(f"min freq filter: {term_min_freq}")
print(f"abuse filter: {term_abuse_threshold}")
apply_filter = True
if apply_filter:
diction_copy.filter_extremes(no_below = term_min_freq, no_above = term_abuse_threshold)
## check
print(diction_copy)
min freq filter: 2 abuse filter: 0.05 Dictionary<40 unique tokens: ['ʃ', 'j', 'ê', 'ɐ', 'ʌ']...>
In [29]:
## Corpus (gensim の用語では corpus) の構築
corpus = [ diction.doc2bow(bot) for bot in bots ]
## check
check = True
if verbose:
sample_n = 5
print(random.sample(corpus, sample_n))
#
print(f"Number of documents: {len(corpus)}")
Number of documents: 5031
In [30]:
## LDA モデルの構築
from gensim.models import LdaModel
#from tqdm import tqdm
## LDAモデル
print(f"Building LDA model with n_topics: {n_topics}")
lda = LdaModel(corpus, id2word = diction, num_topics = n_topics, alpha = 0.01)
#
print(lda) # print(..)しないと中身が見れない
Building LDA model with n_topics: 15 LdaModel<num_terms=80, num_topics=15, decay=0.5, chunksize=2000>
In [31]:
%%capture --no-display
## LDA のtopic ごとに,関連度の高い term を表示
import pandas as pd
n_terms = 20 # topic ごとに表示する term 数の指定
topic_dfs = [ ]
for topic in range(n_topics):
terms = [ ]
for i, prob in lda.get_topic_terms(topic, topn = n_terms):
terms.append(diction.id2token[ int(i) ])
#
topic_dfs.append(pd.DataFrame([terms], index = [ f'topic {topic+1}' ]))
#
topic_term_df = pd.concat(topic_dfs)
## Table で表示
topic_term_df.T
Out[31]:
| topic 1 | topic 2 | topic 3 | topic 4 | topic 5 | topic 6 | topic 7 | topic 8 | topic 9 | topic 10 | topic 11 | topic 12 | topic 13 | topic 14 | topic 15 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | e | a | a | ʁ | ̃ | e | l | ɪ | n | n | h | s | t | e | e |
| 1 | d | e | n | ɛ | ɔ | s | e | ə | e | t | n | a | s | a | r |
| 2 | i | l | l | i | ʁ | b | i | ɹ | z | s | a | u | a | i | n |
| 3 | n | t | e | d | s | o | t | t | a | ː | t | f | e | n | i |
| 4 | f | u | p | e | ɑ | l | a | l | ɔ | f | m | t | ʁ | t | o |
| 5 | u | s | ʃ | a | j | c | f | n | s | l | s | e | ɛ | g | t |
| 6 | r | ː | s | z | i | a | s | s | k | ɪ | l | r | n | c | u |
| 7 | m | ʁ | t | ə | t | p | n | k | w | ɛ | e | l | k | r | p |
| 8 | x | b | i | k | a | r | o | d | o | i | u | d | i | s | a |
| 9 | a | g | ɛ | s | ɛ | m | p | m | p | k | c | é | m | u | v |
| 10 | c | ɔ | w | n | n | n | r | ɛ | ɹ | ə | ʌ | ɐ | v | m | m |
| 11 | t | o | m | t | e | k | d | b | ə | z | b | b | z | o | d |
| 12 | l | j | ɪ | u | k | y | b | e | d | ʃ | ə | o | ɪ | h | l |
| 13 | p | h | f | l | ə | h | k | p | ʁ | u | ü | p | p | l | c |
| 14 | o | n | ː | ʒ | m | i | u | a | g | ɹ | d | c | o | d | s |
| 15 | s | d | k | ɔ | f | ʁ | g | ŋ | ʊ | a | z | ː | r | k | g |
| 16 | h | f | z | v | p | ɛ | m | ʊ | f | ʊ | r | i | y | w | f |
| 17 | é | m | r | m | l | é | c | ɑ | u | ɐ | k | _ | ʃ | f | b |
| 18 | k | y | ʁ | p | b | u | h | æ | b | v | i | ʁ | ɔ | p | é |
| 19 | ʁ | ɡ | d | o | o | t | y | ɡ | i | e | ɛ | œ | ə | b | h |
In [32]:
%%capture --no-display
## pyLDAvis を使った結果 LDA の可視化: 階層クラスタリングより詳しい
import pyLDAvis
#installed_version = sys.version
installed_version = pyLDAvis.__version__
print(f"installed_version: {installed_version}")
if float(installed_version[:3]) > 3.1:
import pyLDAvis.gensim_models as gensimvis
else:
import pyLDAvis.gensim as gensimvis
#
pyLDAvis.enable_notebook()
#
lda_used = lda
corpus_used = corpus
diction_used = diction
## 実行パラメター
use_tSNE = False
if use_tSNE:
vis = gensimvis.prepare(lda_used, corpus_used, diction_used, mds = 'tsne',
n_jobs = 1, sort_topics = False)
else:
vis = gensimvis.prepare(lda_used, corpus_used, diction_used,
n_jobs = 1, sort_topics = False)
#
pyLDAvis.display(vis)
## 結果について
## topic を表わす円の重なりが多いならn_topics が多過ぎる可能性がある.
## ただし2Dで重なっていても,3Dなら重なっていない可能性もある
Out[32]:
In [33]:
## LDA がD に対して生成した topics の弁別性を確認
## 得られたtopics を確認
topic_dist = lda.get_topics()
if verbose:
topic_dist
In [34]:
## 検査 1: topic ごとに分布の和を取る
print(topic_dist.sum(axis = 1))
[0.99999994 1. 1. 0.9999998 0.9999999 1. 1. 1. 1. 0.9999999 1. 1.0000001 1. 1. 1.0000001 ]
In [35]:
## 検査 2: 総和を求める: n_topics にほぼ等しいなら正常
print(topic_dist.sum())
14.999999
In [36]:
## term エンコード値の分布を確認
import matplotlib.pyplot as plt
plt.figure(figsize = (4,5))
sampling_rate = 0.3
df_size = len(topic_dist)
sample_n = round(df_size * sampling_rate)
topic_sampled = random.sample(list(topic_dist), sample_n)
T = sorted([ sorted(x, reverse = True) for x in topic_sampled ])
plt.plot(T, range(len(T)))
plt.title("Distribution of sorted values ({sample_n} samples) for topic/term encoding")
plt.show()
In [37]:
## tSNE を使った topics のグループ化 (3D)
from sklearn.manifold import TSNE
import numpy as np
## tSNE のパラメターを設定
## n_components は射影先の空間の次元: n_components = 3 なら3次元空間に射影
## perplexity は結合の強さを表わす指数で,値に拠って結果が代わるので,色々な値を試すと良い
#perplexity_val = 10 # 大き過ぎると良くない
top_perplexity_reduct_rate = 0.3
perplexity_val = round(len(topic_dist) * top_perplexity_reduct_rate)
topic_tSNE_3d = TSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)
## データに適用
top_tSNE_3d_fitted = topic_tSNE_3d.fit_transform(np.array(topic_dist))
In [38]:
## Plotlyを使って tSNE の結果の可視化 (3D)
#import plotly.express as pex
import plotly.graph_objects as go
import numpy as np
top_tSNE = top_tSNE_3d_fitted
fig = go.Figure(data = [go.Scatter3d(x = top_tSNE[:,0], y = top_tSNE[:,1], z = top_tSNE[:,2],
mode = 'markers')])
## 3D 散布図にラベルを追加する処理は未実装
title_val = f"3D tSNE view for LDA (#topics: {n_topics}, doc: {doc_type}, term: {term_size} {term_type})"
fig.update_layout(autosize = False,
width = 600, height = 600, title = title_val)
fig.show()
In [39]:
## 構築した LDA モデルを使って文(書)を分類する
## .get_document_topics(..) は minimu_probability = 0としないと
## topic の値が小さい場合に値を返さないので,
## パラメター
ntopics = n_topics # LDA の構築の最に指定した値を使う
check = False
encoding = [ ]
for i, row in df.iterrows():
if check:
print(f"row: {row}")
doc = row[doc_type]
bot = row[term_type]
## get_document_topics(..) では minimu_probability = 0 としないと
## 値が十分に大きな topics に関してだけ値が取れる
enc = lda.get_document_topics(diction.doc2bow(bot), minimum_probability = 0)
if check:
print(f"enc: {enc}")
encoding.append(enc)
#
len(encoding)
Out[39]:
5031
In [40]:
## enc 列の追加
#df['enc'] = np.array(encoding) # This flattens arrays
#df['enc'] = list(encoding) # ineffective
df['enc'] = [ list(map(lambda x: x[1], y)) for y in encoding ]
if verbose:
df['enc']
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_13188/1047258704.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [41]:
## エンコーディングのstd の分布を見る
from scipy.stats import tstd
from matplotlib import pyplot as plt
plt.figure(figsize = (6,4))
std_data = [ tstd(x) for x in df['enc'] ]
plt.hist(std_data)
plt.title("Distribution of standard deviations")
plt.show()
In [42]:
## doc のエンコーディング
## 一様分布の事例を除外
from scipy.stats import tstd # standard deviation の計算用
print(f"{len(df)} instances before filtering")
check = False
doc_enc = df['enc']
max_std = max([ tstd(x) for x in doc_enc])
if check: print(f"std max: {max_std}")
min_std = min([ tstd(x) for x in doc_enc])
if check: print(f"std min: {min_std}")
first_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-0]
print(f"std 1st min: {first_min_std}")
second_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-1]
print(f"std 2nd min: {second_min_std}")
5031 instances before filtering std 1st min: 0.13581355754313454 std 2nd min: 0.2547253560162843
In [43]:
## df_filtered の定義
## 閾値は2番目に小さい値より小さく最小値よりは大きな値であるべき
std_threshold = second_min_std / 4 # 穏健な値を得るために4で割った
print(f"std_threshold: {std_threshold}")
## Rっぽい次のコードは通らない
#df_filtered = df[ df['encoding'] > std_threshold ]
## 通るのは次のコード: Creating a list of True/False and apply it to DataFrame
std_tested = [ False if tstd(x) < std_threshold else True for x in df['enc'] ]
df_filtered = df[ std_tested ]
#
print(f"{len(df_filtered)} instances after filtering ({len(df) - len(df_filtered)} instances removed)")
std_threshold: 0.06368133900407108 5031 instances after filtering (0 instances removed)
In [44]:
## doc エンコード値の分布を確認
sample_n = 50
E = sorted([ sorted(x, reverse = True) for x in df_filtered['enc'].sample(sample_n) ])
plt.figure(figsize = (5,5))
plt.plot(E, range(len(E)))
plt.title(f"Distribution of sorted encoding values for sampled {sample_n} docs")
plt.show()
In [45]:
len(df_filtered['language'])
Out[45]:
5031
In [46]:
## tSNE 用の事例サンプリング = tSNE_df の定義
tSNE_sampling = True
tSNE_sampling_rate = 0.33
if tSNE_sampling:
tSNE_df_original = df_filtered.copy()
sample_n = round(len(tSNE_df_original) * tSNE_sampling_rate)
tSNE_df = tSNE_df_original.sample(sample_n)
print(f"tSNE_df has {len(tSNE_df)} rows after sampling")
else:
tSNE_df = df_filtered
tSNE_df has 1660 rows after sampling
In [47]:
tSNE_df.columns
Out[47]:
Index(['form', 'spell', 'sound', 'freq', 'english', 'esperanto', 'french',
'german', 'icelandic', 'russian', 'swahili', 'size', 'language',
'1gram', '2gram', '3gram', 'skippy2gram', 'skippy3gram', 'enc'],
dtype='object')
In [48]:
## tSNE の結果の可視化: Plotly を使った 3D 描画
import numpy as np
from sklearn.manifold import TSNE as tSNE
import plotly.express as pex
import plotly.graph_objects as go
import matplotlib.pyplot as plt
## tSNE のパラメターを設定
perplexity_max_val = round(len(tSNE_df)/4)
for perplexity_val in range(5, perplexity_max_val, 30):
## tSNE 事例の生成
tSNE_3d_varied = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)
## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d_varied = tSNE_3d_varied.fit_transform(doc_enc)
T = zip(doc_tSNE_3d_varied[:,0], doc_tSNE_3d_varied[:,1], doc_tSNE_3d_varied[:,2],
tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])
## 作図
fig = go.Figure()
for lang in np.unique(df['language']):
part = df[df['language'] == lang]
fig.add_trace(
go.Scatter3d(
x = part['D1'], y = part['D2'], z = part['D3'],
name = lang, mode = 'markers', marker = dict(size = 6),
showlegend = True
)
)
title_val = f"tSNE 3D map (ppl: {perplexity_val}) of {doc_attr}s encoded by LDA ({n_topics} topics; term: {term_type})"
fig.update_layout(title = dict(text = title_val),
autosize = False, width = 600, height = 600,)
fig.show()
In [49]:
## 階層クラスタリングのための事例のサンプリング
hc_sampling_rate = 0.1 # 大きくし過ぎると図が見にくい
df_size = len(tSNE_df)
hc_sample_n = round(df_size * hc_sampling_rate)
hc_df = tSNE_df.sample(hc_sample_n)
##
print(f"{hc_sample_n} rows are sampled")
hc_df['language'].value_counts()
166 rows are sampled
Out[49]:
language french 60 english 56 german 50 Name: count, dtype: int64
In [50]:
## doc 階層クラスタリングの実行
import numpy as np
import plotly
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
## 距離行列の生成
Enc = list(hc_df['enc'])
linkage = linkage(Enc, method = 'ward', metric = 'euclidean')
## 描画サイズの指定
plt.figure(figsize = (5, round(len(hc_df) * 0.15))) # This needs to be run here, before dendrogram construction.
## 事例ラベルの生成
label_vals = [ x[:max_doc_size] for x in list(hc_df[doc_type]) ] # truncate doc keys
## 樹状分岐図の作成
dendrogram(linkage, orientation = 'left', labels = label_vals, leaf_font_size = 7)
## 描画
plt.title(f"Hierarchical clustering of (sampled) {len(hc_df)} (= {100 * hc_sampling_rate}%) {doc_attr}s as docs\n \
encoded via LDA ({n_topics} topics) with {term_type} as terms")
## ラベルに language に対応する色を付ける
lang_colors = { lang_name : i for i, lang_name in enumerate(np.unique(hc_df['language'])) }
ax = plt.gca()
for ticker in ax.get_ymajorticklabels():
form = ticker.get_text()
row = hc_df.loc[hc_df[doc_type] == form]
#lang = row['language']
lang = row['language'].to_string().split()[-1] # trick
try:
lang_id = lang_colors[lang]
except (TypeError, KeyError):
print(f"color encoding error at: {lang}")
#
ticker.set_color(plotly.colors.qualitative.Plotly[lang_id]) # id の基数調整
#
plt.show()
In [51]:
## tSNE の結果の可視化 (2D)
#import seaborn as sns
import numpy as np
import plotly
import plotly.express as pex
import matplotlib.pyplot as plt
from adjustText import adjust_text
## tSNE 事例の生成
perplexity_selected = 250
tSNE_3d = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_selected, n_iter = 1000)
## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d = tSNE_3d.fit_transform(doc_enc)
T = zip(doc_tSNE_3d[:,0], doc_tSNE_3d[:,1], doc_tSNE_3d[:,2],
tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])
## 描画
plt.figure(figsize = (5, 5))
plt.set_colors = pex.colors.qualitative.Plotly
for r in [ np.roll([0,1,2], -i) for i in range(0,3) ]:
if check:
print(r)
X, Y = df.iloc[:,r[0]], df.iloc[:,r[1]]
gmax = max(X.max(), Y.max())
gmin = min(X.min(), Y.min())
plt.xlim(gmin, gmax)
plt.ylim(gmin, gmax)
colormap = pex.colors.qualitative.Plotly
lang_list = list(np.unique(tSNE_df['language']))
cmapped = [ colormap[lang_list.index(lang)] for lang in df['language'] ]
scatter = plt.scatter(X, Y, s = 40, c = cmapped, edgecolors = 'w')
## 文字を表示する事例のサンプリング
lab_sampling_rate = 0.02
lab_sample_n = round(len(tSNE_df) * lab_sampling_rate)
sampled_keys = [ doc[:max_doc_size] for doc in random.sample(list(tSNE_df[doc_type]), lab_sample_n) ]
## labels の生成
texts = [ ]
for x, y, s in zip(X, Y, sampled_keys):
texts.append(plt.text(x, y, s, size = 9, color = 'blue'))
## label に repel を追加: adjustText package の導入が必要
adjust_text(texts, force_points = 0.2, force_text = 0.2,
expand_points = (1, 1), expand_text = (1, 1),
arrowprops = dict(arrowstyle = "-", color = 'black', lw = 0.5))
#
plt.title(f"tSNE (ppl: {perplexity_selected}) 2D map of {len(tSNE_df)} {doc_attr}s via LDA ({term_type}; {n_topics} topics)")
#plt.legend(np.unique(cmapped))
plt.show()
In [ ]: